This guide shows how to automate your outbound sales workflow using OpenAI’s Codex plan. It walks through setting up signal detection, scoring accounts, drafting messages, reviewing outputs, logging results, and running a daily cron job that learns from outcomes. The goal is to spot buying triggers early and feed a human-approved message queue each morning.

• Sortlist automated their outbound workflow with Codex, filtering raw signals (CRM data, job posts, funding news) down to only factual, high-intent triggers before drafting messages
• A 70/100 scoring threshold caps daily output at five solid drafts, and a separate check job screens drafts for buzzwords or weak claims before human review
• Morning review of twenty accounts takes under ten minutes thanks to this pre-filtering and scoring pipeline
• Weekly, Codex analyzes logged outcomes (replies, losses, reasons like "no budget") to self-tune its scoring rules and banned phrases, sharpening targeting over time

The article breaks down the recently leaked source code of Anthropic's Claude Code CLI. It highlights the system's architecture, design choices, and differences from OpenAI's Codex, particularly in handling context overflow and user interactions. Key features like compaction strategies and internal versus external user instructions are explored.

• Claude Code uses a four-tiered compaction strategy (proactive token monitoring, reactive fallback, and a "snip compaction" mode for headless sessions) versus Codex's simpler diff-based approach that just minimizes data sent per turn
• The system prompt uses a boundary marker to cache roughly 3,000 tokens of static instructions across users for performance gains
• Internal users get specialized instructions specifically designed to stop the model from misrepresenting test results or giving misleading status updates
• The codebase includes feature flags and build-time checks specifically to prevent sensitive information from leaking into public builds

This plugin embeds OpenAI Codex into your Claude Code workflow, letting you run standard, adversarial, or rescue reviews without switching tools. Install via Node.js, authenticate with your ChatGPT subscription or API key, then use /codex:review, /codex:adversarial-review, and /codex:rescue alongside status commands.

• Claude Code now has an official Codex plugin (from openai/codex-plugin-cc) with three review modes: standard, adversarial, and rescue
• Adversarial review is meant for high-stakes changes like migrations, auth, or infra scripts to catch subtle flaws standard review might miss
• It runs through your existing Codex CLI/server, so local auth, config, and MCP setup carry over automatically
• An optional "review gate" can force Codex review before Claude Code finishes, but risks tight loops that rapidly burn usage limits

OpenAI is rolling out a new model called GPT-5.2-Codex-Max for subscribers, which enhances the capabilities of its Codex tool. This version improves performance on long tasks, tool use reliability, and understanding of visual content, building on the features introduced in GPT-5.2. Further details about the model are expected to be released soon.

• OpenAI is rolling out GPT-5.2-Codex-Max, a new Codex model, to some subscribers
• It improves performance on long tasks, tool use reliability, and visual content understanding compared to GPT-5.2
• More details on the model are expected to be released soon